Classification of Immission Measurements(1)
Dr. Alf Loeffler
10/31/2021
Classification of Immission Measurements 2000 to 2020 in Baden Wuerttemberg
longterm trends calculated from recordings at sampling stations
location of selected sampling stations
20 sampling stations operated by LUBW (Landesanstalt für Umwelt Baden-Wuerttemberg) have been selected for analysis of concentrations of atmospheric constituents.In addition 2 stations operated by the city of Stuttgart “Amt für Umweltschutz”.
| ID | name | Messstelle | Ost_UTM | Nord_UTM |
|---|---|---|---|---|
| 47650 | Alb | Schwäbische Alb | 515300 | 5354700 |
| 4467 | Sws | Schwarzwald-Süd | 407500 | 5295900 |
| 76118 | Odw | Odenwald | 482100 | 5479100 |
| 76359 | Lbg_Friedr | Ludwigsburg Friedrichstraße | 513900 | 5415100 |
| 76364 | Rt_leder | Reutlingen Lederstraße-Ost | 515500 | 5370700 |
| 76361 | Nck | Stuttgart Am Neckartor | 514000 | 5403900 |
| 39253 | Brn | Bernhausen | 516700 | 5391600 |
| 4445 | Eggenstein | Eggenstein | 456600 | 5436100 |
| 4462 | Frei | Freiburg | 412800 | 5317100 |
| 4471 | Friedri | Friedrichshafen | 536500 | 5278600 |
| 4453 | Heid | Heidelberg | 476500 | 5474100 |
| 4461 | Heil | Heilbronn | 516300 | 5445800 |
| 4463 | Lbg_weimar | Ludwigsburg | 512600 | 5416200 |
| 4470 | Rt_pomol | Reutlingen | 515300 | 5370600 |
| 4452 | Stuttgart-Bad Cannstatt | Stuttgart-Bad Cannstatt | 516800 | 5406200 |
| 55006 | Stgt_Klett | Stuttgart Arnulf-Klett-Platz | 513200 | 5403300 |
| 9999136 | Hauptst_str | Stuttgart Hauptstätter Straße | 512500 | 5401500 |
| 9999137 | Stg_Stadtg | Stuttgart Stadtgarten | 512600 | 5403100 |
| 4473 | Man_Mitte | Mannheim-Mitte | 462100 | 5480400 |
| 76362 | Stuttgart Hohenheimer Straße | Stuttgart Hohenheimer Straße | 513500 | 5401700 |
| xxx | Schwabenzentrum | Schwabenzentrum | NA | NA |
| xx1 | Amt_Umwelt | Amt Umweltschutz | NA | NA |
3 sampling stations are located in rural environment, 7 stations located at main streets, 10 sampling urban background immissions. In addition 2 stations operated by the city of Stuttgart one located downtown on top of a building about 20 m above the street level the second at the office building in urban background. An overview of the locations:
## # A tibble: 22 × 5
## ID name Messstelle Ost_UTM Nord_UTM
## <fct> <fct> <fct> <dbl> <dbl>
## 1 47650 Alb Schwäbische Alb 515300 5354700
## 2 4467 Sws Schwarzwald-Süd 407500 5295900
## 3 76118 Odw Odenwald 482100 5479100
## 4 76359 Lbg_Friedr Ludwigsburg Friedrichstraße 513900 5415100
## 5 76364 Rt_leder Reutlingen Lederstraße-Ost 515500 5370700
## 6 76361 Nck Stuttgart Am Neckartor 514000 5403900
## 7 39253 Brn Bernhausen 516700 5391600
## 8 4445 Eggenstein Eggenstein 456600 5436100
## 9 4462 Frei Freiburg 412800 5317100
## 10 4471 Friedri Friedrichshafen 536500 5278600
## # … with 12 more rowsAs example of NO2 1-h mean values, measured at Stuttgart- Bad Cannstatt at the roadside are shown in the plot “NO2-immissions 20 years”. The 1- h means are plotted together with red linear regression line over 20 years:
Stg_Can
The following should be noted:
– the immissions vary annually with maximum in winter month – average immissions are reduced with a rate of about \(0.4 [μg/m^3]per year\) – there are long periods without zero 1-h measurements (2016 to 2018) and others reaching zero over longer time periods(2001) – outliers indicate a strong variation of the immissions at the location of the sampling instrument
These observations need to be taken into account with interpretation of the data.
Key immissions data from 23 sampling stations
Mean values, variance and median of \(NO_2\) and \(O_3\) concentrations are very different for the 22 stations analyzed:
| Station | NO2_mean | NO2_median | NO2_var | WG_mean | WG_var | O3_mean | O3_median | O3_var |
|---|---|---|---|---|---|---|---|---|
| Sws | 3.0 | 2.0 | 11.9 | 2.4 | 15.2 | 75.0 | 73 | 366.8 |
| Alb | 7.1 | 5.0 | 50.4 | 3.3 | 4.7 | 67.3 | 66 | 874.9 |
| Odw | 10.7 | 8.0 | 85.3 | 2.0 | 1.9 | 66.1 | 62 | 1014.5 |
| Frei | 20.5 | 16.0 | 234.5 | 1.7 | 1.8 | 51.3 | 51 | 1291.8 |
| Egg | 23.5 | 20.0 | 252.0 | 2.4 | 3.6 | 42.0 | 38 | 1248.5 |
| Fri | 24.2 | 21.0 | 242.8 | 1.0 | 0.8 | 44.1 | 40 | 1189.0 |
| Rt_pomol | 25.7 | 22.0 | 298.5 | 1.3 | 0.8 | 46.6 | 45 | 1114.4 |
| Stg_SZ_afu | 25.9 | 22.8 | 231.7 | 1.7 | 0.9 | NA | NA | NA |
| Heid | 27.6 | 25.0 | 283.5 | 1.5 | 0.9 | 45.0 | 41 | 1138.0 |
| Stg_Stadtg | 27.8 | 22.0 | 353.8 | NA | NA | NA | NA | NA |
| Lbg_weimar | 28.1 | 24.0 | 375.6 | 1.8 | 1.2 | 45.5 | 42 | 1305.0 |
| Stg_Schwz | 28.1 | 25.0 | 287.5 | 1.6 | 0.8 | NA | NA | NA |
| Brn | 30.8 | 26.0 | 419.4 | 1.6 | 1.8 | 40.8 | 37 | 1165.5 |
| Heil | 30.9 | 27.0 | 404.5 | 1.5 | 1.3 | 40.2 | 34 | 1319.1 |
| Stg_Can | 31.6 | 29.0 | 364.2 | 1.1 | 0.6 | 40.8 | 35 | 1256.2 |
| Man_Mitte | 34.2 | 30.0 | 441.2 | 1.5 | 1.2 | NA | NA | NA |
| Stg_Hpt | 44.1 | 41.0 | 412.3 | NA | NA | NA | NA | NA |
| Lbg_Friedr | 54.4 | 52.0 | 661.6 | NA | NA | NA | NA | NA |
| Stg_Klt | 64.0 | 59.0 | 995.7 | NA | NA | 64.3 | 45 | 3884.1 |
| Rt_leder | 70.1 | 66.0 | 1258.9 | NA | NA | NA | NA | NA |
| Stg_Hoh | 78.9 | 71.0 | 2094.1 | NA | NA | NA | NA | NA |
| Stg_Nck | 87.5 | 79.0 | 2422.1 | NA | NA | NA | NA | NA |
The table is arranged in ascending order of mean NO2 immissions measurements calculated from 1-h measurements recorded and published by LUBW (Landesanstalt für Umwelt Baden Wuerttemberg). The mean calculated on the 20 year interval of all recorded data.
–in rural environment immissions are less than \(10 [μg/m^3]\).
–urban background immissions range from \(10 [μg/m^3]\) to \(30 [μg/m^3]\).
–immissions close to main traffic streets are bigger than \(40 [μg/m^3]\).
linear trends of immissions at sampling stations
BW_NO2 trends
Some observations from the trend :
– average immissions at the three rural stations stay below \(10 [μg/m^3]\)
– at the rural stations the mean ** $mean NO_2 $ ** values are independent of the time – the regression lines (20 year mean) from stations located in urban background
are in between \(25 [μg/m^3]\) and \(40 [μg/m^3]\) – at background stations rate of reduction of $mean NO_2 $ ** is low** compared with traffic stations
– at main streets (heavy traffic) the reduction per year of $mean NO_2 $ is high
– 20-year $mean NO_2 $ exceeded limits considered dangerous to human health close to main streets – at Stuttgart “Am Neckartor” the highest $mean NO_2 $ were recorded
— starting in 2003 with annual mean greater \(125 [μg/m^3]\) which were reduced to \(50 [μg/m^3]\)in 2020
— long trend “Am Neckartor” indicates levels smaller \(40 [μg/m^3]\) – at some stations the rate of reduction (chg_py) is higher than at the station “Am Neckartor”
rate of immission reductions
## datetime
## -0.05078936| Station | chg_py |
|---|---|
| Stg_Hpt | -5.1 |
| Stg_Schwz | -5.0 |
| Stg_Nck | -4.3 |
| Rt_leder | -4.0 |
| Stg_Hoh | -3.6 |
| Stg_SZ_afu | -2.8 |
| Lbg_Friedr | -2.8 |
| Stg_Klt | -1.9 |
| Stg_Stadtg | -0.9 |
| Man_Mitte | -0.6 |
| Lbg_weimar | -0.6 |
| Heid | -0.5 |
| Stg_Can | -0.4 |
| Heil | -0.4 |
| Rt_pomol | -0.3 |
| Brn | -0.3 |
| Egg | -0.3 |
| Fri | -0.2 |
| Frei | -0.2 |
| Odw | -0.1 |
| Alb | -0.1 |
| Sws | 0.0 |
The mean yearly reductions ranges from \(-5.1[μg/(m^3*y)]\) at the “Hauptstätter Strasse” and no change at all \(0 [μg/(m^3*y)]\) at the rural background station “Schwarzwald-Süd”.
## Length Class Mode
## Alb 9 -none- list
## Sws 10 -none- list
## Odw 10 -none- list
## Brn 6 -none- list
## Lbg 5 -none- list
## Rt 9 -none- list
## Stg.Nck 2 -none- list
## Stg.Hpt 2 -none- list
## Stg.Can 8 -none- list
## Stg.Klt 4 -none- list
## Rt.l 3 -none- list
## Lbg.fr 2 -none- list
## Stg.Hoh 2 -none- list
## Man_Mitte 9 tbl_df list## Joining, by = c("station", "datetime")
## Joining, by = c("station", "datetime")
## Joining, by = c("station", "datetime")## [1] 8## `geom_smooth()` using formula 'y ~ x'
## Effect of Heating on $NO_2, NO, O_3
dat_path<- "~/documents/Luftqualitaet/Daten/BW_Rdat"
load(file.path(dat_path,"BW.RData"))
summary(BW.all_data)## Length Class Mode
## Alb 9 -none- list
## Sws 10 -none- list
## Odw 10 -none- list
## Brn 6 -none- list
## Lbg 5 -none- list
## Rt 9 -none- list
## Stg.Nck 2 -none- list
## Stg.Hpt 2 -none- list
## Stg.Can 8 -none- list
## Stg.Klt 4 -none- list
## Rt.l 3 -none- list
## Lbg.fr 2 -none- list
## Stg.Hoh 2 -none- list
## Man_Mitte 9 tbl_df listCan_data<-BW.all_data$Stg.Can$Stg.Can.no2 %>% left_join(BW.all_data$Stg.Can$Stg.Can.no)## Joining, by = c("station", "datetime")Can_data<-Can_data%>% left_join(BW.all_data$Stg.Can$Stg.Can.temp)## Joining, by = c("station", "datetime")Can_data<-Can_data%>%left_join(BW.all_data$Stg.Can$Stg.Can.o3)## Joining, by = c("station", "datetime")Can_data_list<-BW.all_data$Stg.Can
Can_comps<-names(BW.all_data$Stg.Can)
length(Can_comps) #8## [1] 8Can_tbl<- BW.all_data$Stg.Can$Stg.Can.no2%>% dplyr::select(-NO2)
for (i in 1:8) {
df <- BW.all_data$Stg.Can[[i]]
Can_tbl <- left_join(Can_tbl,df, by=c("station", "datetime"))
}
Can_tbl<-Can_tbl%>% mutate(Hzg = as_factor(ifelse(Temp<= 15,"heating", "no_heating")))
comps <- names(Can_tbl)%>%setdiff(c("station","datetime","Hzg"))
Can_tidy<- Can_tbl%>% pivot_longer(cols = all_of(comps),names_to = "comp",values_to = "value" )
# Plot
Can_tidy %>% na.omit()%>% filter(comp!= "WR"& comp != "WG"& comp != "CO"& comp != "SO2") %>%
ggplot(aes(x = datetime))+
geom_smooth(method = "lm",mapping = aes(y= value,col= comp))+
facet_grid(.~Hzg)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
ggtitle(" 21-Year-Trend(regression)
NO, NO2, O3, Temperature",
subtitle = "Heating hours Stgt.-Bad Cannstatt")+
labs(x= "", y = "Values(μg/m3,m/s,°C)")## `geom_smooth()` using formula 'y ~ x'
distribution of heating hours
To visualize the effect of energy burned to heat individual homes, offices and public buildings the demand of heating expressed by the temperature difference \(Grdz = 20- Temp [°C]\) Room temperature an outside Temperature is classified into 4 groups: - no heating necessary \(no.ht\)
- low heating \(low.ht\)
- medium heating \(medium.ht\) - maximum heating \(max.ht\)
Within 21 years (183960 hours) about one third ~ 60000 hours no heating was necessary, low energy consumption was necessary at about 80000 hours, medium with ~ 20000 hours and maximum only for a very small number of hours. 
Comparison with emission data
The graph shows the emissions reported to or calculated by the German Umweltbundesamt (UBA). The regression lines the average reduction of total emissions and the contribution of transport and road transport. The main reason for the reduction of transport emissions were improvements were progress with combustion engines and intensive treatment of exhaust gases at power plants and industry. the latter took place mainly 2000 to 2005.